Modeling the Factors Influencing the Retention Decision of ...

LPU-Laguna Journal of Multidisciplinary Research

Vol. 4 No. 1 October 2020

31

Center for Research, Publication, and Intellectual Property

Modeling the Factors Influencing the Retention Decision of the Students of LPU-Laguna

and LPU-St. Cabrini using Students Integration Theory

Dr. Ryan Jeffrey Curbano, Dr. Noel B. Hernandez

College of Engineering and Computer Studies, Centro Escolar University Graduate School

ABSTRACT

The study was conducted to determine the factors that influence the student retention at LPU Laguna and

LPU-St. Cabrini. The study was guided by Tinto’s Student Integration Theory focusing the students view

on academic integration (faculty concern for student development and teaching and academic and

intellectual development), social integration (peer group interaction and faculty interaction), and

institutional and goal commitment. A total of 500 students were surveyed in this study. Stratified random

sampling was used as sampling design and quantitative descriptive statistics was used as research design.

The results revealed that majority of respondents were aged 20-22, female, 3rd year level, with more than

50,000 monthly income, and 2.01-2.50 general weighted average. In terms of assessment on factors

influencing the student retention, institutional and goal commitment had highest assessment while

academic and intellectual development had the lowest assessment. Findings showed that the measurement

model satisfies the requirement of exploratory and confirmatory factor analysis. Furthermore, faculty

concern for student development and teaching, peer group interaction, faculty interaction and

institutional and goal commitment were found significant factors of student retention. While in terms of

factors of retention decision and demographic profile, sex and GWA/GPA had significant relationship on

the student retention. The researchers suggested to continue faculty and student consultation, student’s

tutorial and enhanced student engagement activities to further improve the satisfaction level of the

students and longer stay in the institution.

Keywords: Student retention, social integration, academic integration, institutional and goal commitment

INTRODUCTION

For academic institutions, retention of students has become a challenging issue. One of the most

frequently researched topics in higher education is student retention (Seidman, 2005). Higher education

institutions have long been concerned with trying to ensure that students stay and finish their studies once

enrolled and that they get as much out of them as they can (Tight M., 2019). Institutions also talk about

levels of retention or graduation rates. Only if they know their students' intentions, the institution can

measure a realistic retention rate.

In many distinct aspects, including their academic and financial plans, low student retention rates

could impact tertiary institutions (Aljohani, 2016). Therefore, it is crucial for educators and policymakers

pursuing solutions to this issue to have an understanding of the context of the phenomenon of student

retention and the most common factors that lead students to leave their study programs.

The relevance of student retention problems is known to private higher education institutions

(HEIs) because the experience of students is a concrete demonstration of the validity and sense of the

institutional purpose (Scholder and Maguire, 2009). Therefore, it is important to request input from

students to determine retention factors about specific areas of the institution such as importance,

resources, academics, teachers, advisory / support services, social life, extracurricular activities,

educational objectives, and preparation for the future.

The process of student admission should be considered in order to see the retention rates in the

higher education institutions and this will be important in deciding how the students remain in the

university. Lyceum of the Philipines University-Laguna (LPU-Laguna) and LPU-St Cabrini School of

Health Sciences (LPU-St. Cabrini) are both higher education institutions in the Philippines that have an

average of 2500 undergraduate students per year and consistently competing with other nearby



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institutions. Similar with other institutions, LPU-Laguna and LPU-St Cabrini are private education

institutions generate their income mainly on the fees paid by the students. This income is largely

dependent on the number of enrollees it attracts and supports all the institution’s operations. Fortunately,

both institutions are ISO 9001:2015 certified and as part of the institutional quality objectives, the student

feedback and student retention rating have been monitored regularly. Both institutions had an overall

retention rate of 92 percent higher than the institutional target of 88 percent in the last academic year

2019-2020 and this was a product of the institutional mission of providing students with quality service.

This study aimed to develop a model based on factors that influenced the retention decisions of

the students in LPU-Laguna and LPU St. Cabrini School of Health Sciences. This study was guided by

Tinto’s student integration theory. The result of the study will guide the administrators of the institutions

in identifying the factors that would make students retain in the institutions using the variables suggested

in Tinto’s Model.

Review of Literature

The definition of retention in educational settings refers to “students' continued study until

successful completion” (Fowler & Luna, 2009). Student retention has been an issue facing higher

education for more than 70 years (J.M. Braxton, 2000). Tinto's (1993) Student Integration Model is one of

the oldest student retention models, in which he suggested an interaction model that laid the theoretical

basis for student attrition research (Weng et al., 2015). The main concept of Tinto’s model is the level of a

student’s integration into the social and academic systems of the college, which determines persistence or

dropout. Tinto (1993) has categorized student retention theories into three types: psychological,

environmental, and interactional. Psychological theories focus on individual personality attributes and

view student attrition as reflecting some shortcoming and/or weakness in the individual. Environmental

theories focus on the social, economic, and organizational forces impacting on student retention. Societal

theories emphasize the importance of social forces that are external to the higher education institution on

student retention such as social status, race, prestige, and opportunity.

As a result, they are insensitive to individual and institution specific forces that affect student

retention decisions. Economic theories emphasize the importance of individual finances and financial aid

in student retention. However, there is little empirical evidence to support the connection that financial

forces are primary influences for most students’ retention decisions. Tinto (1993) suggests that financial

variables appear to be of secondary significance to most students' choices. He suggests two reasons for

this; first the influence of finance on retention is more influential in college entry decisions than college

retention decisions. Secondly, while students often discuss financial reasons for leaving, other factors not

associated with finance are often their key reasons. When students have a positive experience at

university, they are often more likely to cope with financial problems in order to continue their study.

Organizational theories focus on the effect of organizational factors on student retention. Factors studied

within these theories include bureaucratic structure and size, faculty student ratios, and institutional

resources and goals. Organizational theories are useful in explaining student retention between higher

education institutions.

According to Tinto’s theory (1993), students enter university with a set of background

characteristics including: family backgrounds (e.g., family social status, parental formal education, and

parental expectations); individual attributes (e.g., gender, race, age, and academic aptitude); and, pre-

college schooling (e.g., high school achievement, academic course work). These background

characteristics combine to influence the initial goal and institutional commitments that the student brings

to the university environment. Goal commitments represent the degree to which the student is committed,

or motivated, to get a university degree in general. Institutional commitments represent the degree to

which the student is motivated to graduate from a specific university. These commitments change during

the student’s time at university as a result of the degree of integration into the academic and social



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systems of the university. In turn, these two types of integration lead to new levels of goal and

institutional commitments. In addition, the student’s initial goal and institutional commitments influence

their later goal and institutional commitments. Tinto states that ‘in the final analysis, it is the interplay

between the individual’s commitment to the goal of college completion, and his commitment to the

institution that determines whether or not the individual decides to drop out from college.

Another influential model is Bean’s model (1980) which was derived from theories of

organizational turnover and planned behavior. Student attrition is viewed as similar to turnover in

business organizations. A complex interaction of internal and external variables influences the direction

of the student’s intentions and ultimately the decision to leave or persist. The model recognized that

factors external to the institution can play a major role in affecting student decisions. Individual higher

education student attrition is viewed as resulting from the following variables: student background

variables, organizational variables, academic integration, social integration, environment variables,

attitudes, grade point average (GPA), institutional fit, institutional commitments/ loyalty, and intention to

leave or persist. Studies using Student Integration Theory suggest that academic integration, social

integration, institutional commitment and goal commitment have the strongest impact on student retention

Finally, an integrated model which combined both Tinto’s and Bean’s models, was developed

(Cabrera, et al, 1993) to offer an integrated framework for understanding the higher education persistence

process. The two models claimed that persistence was affected by the integration between the students

and the institution. Overall, the results of the integrated model support the propositions claimed in both

models. For example, the relationships among academic and social integration constructs, as well as those

among commitment constructs, are consistent in both models. Furthermore, support was also found for

external factors influencing.

Conceptual Framework

Figure 1 shows the research framework of which the individual characteristics such as age, sex,

year level, GWA/GPA, and monthly family income. The faculty concern for student development and

teaching, academic and intellectual development, peer group interaction, interaction with faculty and

institutional and goal commitment were the identified variables used in Tinto’s Student Integration

Theory. Social and academic integration in relation to the engagement of a student to the university is the

principal point of Tinto's theory. It is also very important that students in both informal and formal ways

will develop social and academic integration skills. Academic integration refers to an assertion that the

integration of a person can be evaluated during the college year in terms of both grade performance and

intellectual growth. The extent and degree of congruence between the person and his or her social

environment refers to social integration (e.g., extracurricular activities and peer-group interactions).

Social integration refers to the informal education of students, unlike academic integration. It focuses on

the relationships of students with peers, faculty, and staff that exist entirely beyond the institution's

education environment (Lyons, 2007).



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Figure 1. Research Framework

While the target commitments of the students address the degree to which they are driven to enter

university, the institutional commitments of the students define the extent to which they are committed to

graduating from a specific university, resulting in the retention decision of the student to remain in the

institution.

Objectives of the Study

The main objective of the study is model the factors that influence the retention decision.

Specifically aimed to answer the following objectives: to present the demographic profile of the students;

assess the factors influencing the retention decision of the students; verify the factors that influence the

retention decision of the students and develop action plan based on the findings of the study.

METHODOLOGY

This research utilized a quantitative approach and descriptive method to determine the outcomes

of the study. The descriptive research design is a scientific method which involves describing the

behavior of a subject matter while not influencing it in any process. The survey questionnaire was used to

gather data to students from LPU-Laguna and LPU-St. Cabrini currently enrolled in flexible learning

scheme. In this research, stratified random sampling was used. The total surveyed was 500 respondents,

63 from College of Arts and Sciences (CAS), 94 from College of Business and Accountancy (CBA), 100

from College of Engineering and Computer Studies (COECS), 100 from College of International Tourism

and Hospitality Management (CITHM), abd 143 from LPU-St. Cabrini’s College of Allied Medicine

(CAM). The total number of respondents is acceptable since the minimum number of respondents for

structural equation modelling is 200 (Kline, 2011).

The survey questionnaire was divided into two parts. The first part encompassed the demographic

profile of the respondents such as age, sex, year level, general weighted average, and monthly income.

While the second part includes the factor variables of Tinto’s Students Integration Theory such as the

faculty concern for student development and teaching, academic and intellectual development, peer group

interaction, interaction with faculty, and institutional and goal commitment. The researchers asked the

approval of the management to conduct this study. The survey questionnaire was distributed to

respondents via Google forms. The results obtained were tabulated, organized, and analyzed using



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Statistical Package for Social Sciences (SPSS) software version 23 and AMOS version 24. The analysis

of the factors influencing the retention decision of the students utilized the structural equation modeling

as statistical treatment of the study. Structural equation modeling refers to a multivariate statistical

analysis technique that is used to analyze structural relationships. This technique is the combination of

factor analysis and multiple regression analysis, and it is used to examine the structural relationship

between measured variables and latent constructs.

RESULTS AND DISCUSSIONS

Demographic Profile of the Respondents

Demographic information provides data regarding the participants of the study. Hammer (2011)

noted that the thorough description of the participants allows the readers and researcherss to determine to

whom the research findings were generalized and the gap of the existing bodies of research can be

identified.

In this context, the researchers determined the profile of the respondents in terms of age, sex, year

level, monthly income, and general weighted average.

Table 1. Demographic profile of the respondents Frequency Percent

Age

17-19 125 25

20-22 349 70

23 and above 26 5

Total 500 100

Sex

Male 143 29

Female 357 71

Total 500 100

Year Level

2nd year 232 46

3rd year 234 47

5th year 34 7

Total 500 100

Monthly Income

10,000-20,000 98 20

20,001-30,000 87 17

30,001-40,000 95 19

40,001-50,000 80 16

more than 50,000 140 28

Total 500 100

General Weighted Average

1.00-1.50 106 21

1.51-2.00 190 38

2.01-2.50 153 31

2.51-3.00 51 10

Total 500 100

Table 1 shows the demographic profile of respondents. The results showed that in terms of age,

349 out of 500 or 70 percent were 20-22 years old, 125 out of 500 or 25 percent were 17-19 years old, and



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26 out of 500 or 5 percent were 23 years old and above. In terms of sex, 357 out 500 or 71 percent were

female, and 143 out of 500 or 29 percent were male. In terms of year level, 234 out of 500 or 47 percent

were 3rd year level, 232 out of 500 or 46 were 2nd year level and 34 out of 500 or 7 percent were 5th year

level. In terms of monthly income, 140 out of 500 or 28 percent had more than 50,000, 98 out of 500 or

20 percent had 10,000-20,000, 95 out of 500 or 19 percent had 30,001-40,000, 87 out of 500 or 17 percent

had 20,001-30,000, and 80 out of 500 or 16 percent had 40,001-50,000. In terms of general weighted

average, 190 out of 500 or 38 percent had 1.51-2.0, 153 out of 500 or 31 percent had 2.01-2.50, 106 out of

500 or 21 percent had 1.00-1.51 and 51 out of 500 or 10 percent had 2.51-3.00.

The results of individual characteristics interplay between the individual’s commitment to the

goal of college completion, and his commitment to the institution determines whether or not the

individual decides to drop out from college (Tinto, 1993). Stratton et al. (2007) used demographic

variables as potential explanatory variables for the variance in student retention in institutions of higher

education.

Factors Influencing the Retention Decision

Table 2 shows the factors influencing the retention decision in terms of faculty concern for

student development and teaching. The results showed that the respondents agreed in the overall

assessment having a composite mean of 3.09. The indicator, “The faculty members I had encountered

with are genuinely interested in teaching” had highest mean of 3.15 and standard deviation of 0.64. While

the indicator, “The faculty members I had encountered with are willing to spend time outside of class to

discuss issues of interest and importance to students” had lowest mean of 2.97 and standard deviation of

0.66. The results supported the study of Cass, Cowie, and Campbell (2009), which says that the lecturer

attributes and abilities may influence the enthusiasm of the students to the subject and the decisions they

make for their future careers crucially important for retention. There is abundant evidence that informal

student-faculty contact outside the classroom correlates positively with student retention. Such

interactions can have a normalizing effect on students’ socialization to the attitudes and values of their

institution. Interactions like these can also lead to an increased bond between students and their university

(Cox, McIntosh, Terenzini, Reason, & Lutovsky Quaye 2010).

Table 2. Factors influencing the retention decision in terms of faculty concern for student

development and teaching

Faculty Concern for Student

Development and Teaching Mean SD Interpretation Rank

The faculty members I had encountered

with are…

generally interested in students 3.11 0.52 Agree 2.5

generally outstanding or superior teachers 3.10 0.57 Agree 4

willing to spend time outside of class to

discuss issues of interest and importance

to students

2.97 0.66 Agree 5

interested in helping students grow in

more than just academic areas 3.11 0.69 Agree 2.5

genuinely interested in teaching 3.15 0.64 Agree 1

Composite Mean 3.09 0.62 Agree

Legend: 1.00-1.74- Strongly Disagree; 1.75-2.49- Disagree; 2.50-3.24-Agree; 3.25-4.00 Strongly Agree



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Table 3 shows the factors influencing the retention decision in terms of academic and intellectual

development. The results showed that the respondents agreed in the overall mean assessment having a

composite mean of 2.89. The indicator, “My academic experience has a positive influence on my

intellectual growth and interest” had the highest mean of 3.03 and standard deviation of 0.63. While the

indicator, “My courses this term has been intellectually stimulating” had lowest mean of 2.80 and

standard deviation of 0.65. The results confirm the findings of Lyons (2007) that the students' academic

and intellectual growth would affect the students' dedication to earn degrees from the institution.

Table 3. Factors influencing the retention decision in terms of academic and intellectual development

Academic and Intellectual

Development Mean SD Interpretation Rank

I am satisfied with the extent of my

intellectual development from the time I

enrolled in LPU

2.93 0.64 Agree 2

My academic experience has a positive

influence on my intellectual growth and

interest.

3.03 0.63 Agree 1

I am satisfied with my academic

experience at LPU 2.82 0.71 Agree 3

My courses this term has been

intellectually stimulating 2.80 0.65 Agree 4



Table 4 shows the factors influencing the retention decision in terms of peer group interaction.

The results showed the respondents agreed in the overall assessment having a composite mean of 3.17.

The indicators, “The student friendship I have developed at this institution have been personally

satisfying” and “My interpersonal relationships with other students have had a positive influence on my

intellectual growth and interest” had the highest mean of 3.27 and standard deviation of 0.67 and 0.60

respectively. While the indicator, “It has been easy for me to meet and make friends with other students”

had lowest mean of 2.90 and standard deviation of 0.75. The results of Pascarella & Terenzini (2005)

confirm that the effect of peers on persistence has become more consistent in suggesting that positive

experiences between peers have a positive impact on the decision to stay in school.

Table 5 shows the factors influencing the retention decision in terms of interaction with faculty.

The results showed that the respondents agreed on overall assessment having composite mean of 2.97.

The indicator, “My interaction with faculty have positive influence on my career goals and aspirations”

had highest mean of 3.10 and standard deviation of 0.61. While the indicator “I have developed a close,

personal relationship with at least one faculty members” had lowest mean of 2.81 and standard deviation

of 0.77. According to Tinto’s model of student attrition, student-faculty interaction should increase not

only the students’ social integration but also their academic integration and this will lead to higher rate of

retention (Pascarella & Terenzini 2005). Positive experiences can boost the academic performance of

students, increase college satisfaction, enhance intellectual and personal growth, increase learning

motivation, and also affect student persistence (Karaivanova, 2016).



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Table 4. Factors influencing the retention decision in terms of academic and intellectual development

Academic and Intellectual

Development Mean SD Interpretation Rank

Since coming to this institution I have

developed close personal relationship

with other students

3.24 0.68 Agree 4

The student friendship I have developed

at this institution have been personally

satisfying

3.27 0.67 Strongly

Agree 1.5

My interpersonal relationships with other

students have had a positive influence on

my personal growth, attitudes and values

3.26 0.64 Strongly

Agree 3

My interpersonal relationships with other

students have had a positive influence on

my intellectual growth and interest

3.27 0.60 Strongly

Agree 1.5

It has been easy for me to meet and make

friends with other students 2.90 0.75 Agree 7

Most of the students I know would be

willing to listen to me and help me if I

had a personal problem

3.04 0.70 Agree 6

Most students at this institution have

values and attitudes different from my

own

3.17 0.61 Agree 5



Table 5. Factors influencing the retention decision in terms of interaction with faculty

Interaction with Faculty Mean SD Interpretation Rank

My interaction with faculty have positive

influence on my personal growth, values

and attitudes

2.99 0.59 Agree 3


influence on my intellectual growth and

interest in ideas

3.01 0.60 Agree 2


influence on my career goals and

aspirations

3.10 0.61 Agree 1

Since coming to this institution I have

developed a close, personal relationship

with at least one faculty members.

2.81 0.77 Agree 5

I am satisfied with the opportunities to

meet and interact informally with faculty

members

2.95 0.65 Agree 4

Composite Mean 2.97 0.64 Agree Legend: 1.00-1.74- Strongly Disagree; 1.75-2.49- Disagree; 2.50-3.24-Agree; 3.25-4.00 Strongly Agree



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Table 6 shows the factors influencing the retention decision in terms of institutional and goal

commitment. The results showed that the respondents agreed in the overall assessment having a mean of

3.19. The indicator, “Getting good grades is important to me” had highest mean of 3.59 and standard

deviation of 0.57 while the indicator, “I am confident that I made right decision in choosing LPU” had

lowest mean of 2.89 and standard deviation of 0.69. According to Alexandros, Ejaz, and Rupert (2017),

students’ goal commitments address the extent to which they are motivated to enter university and

students’ institutional commitments describe the extent to which they are committed to graduating from a

particular university.

Table 6. Factors influencing the retention decision in terms of institutional and goal commitment

Institutional and Goal Commitment Mean SD Interpretation Rank

I am confident that I made right decision

in choosing LPU 2.89 0.69 Agree 4

It is likely that I will still enroll in LPU

next term 3.10 0.63 Agree 3

It is important to me to graduate from this

institution 3.17 0.71 Agree 2

Getting good grades is important to me 3.59 0.57 Strongly

Agree 1



Modeling the Factors Influencing the Retention Decision

Exploratory Factor Analysis

The research had tested the measures of sampling adequacy using Kaiser- Meyer- Olkin (KMO)

which indicates the proportion of variance in the variables caused by underlying factors. The Bartlett’s

test of sphericity is a statistical test used to measure the presence of correlations among the variables. It

generates the correlation matrix has significant statistical relationships among at least some of the

variables (Hair et al., 2014).

Table 7 presents the test of sampling adequacy and the result of KMO is 0.9370 and Bartlett's test

of sphericity resulted to a significance value of .000 and approximate chi-square value of 7245. The result

shows the acceptance of factor analysis was satisfied. The measure can be interpreted with the following

guidelines: .80 or above, meritorious; .70 or above, middling; .60 or above, mediocre; .50 or above,

miserable; and below .50, unacceptable. The measurement of sampling adequacy value should be above

.50 before proceeding with factor analysis. A statistically significant Bartlett’s test of sphericity (sig. <

.05) indicates that sufficient correlations exist among the variables to continue (Hair et al., 2014).

Table 7. Test of sampling adequacy

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy. 0.9370

Bartlett's Test of Sphericity Approx. Chi-

Square 7245

df 300

Sig. 0.000



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Construct Validity and Confirmatory Factor Analysis

Construct validity was used to determine the extent to which a set of measured items reflected the

latent theoretical, and it deal with the accuracy of measurement (Hair et al., 2014).

Table 8 displays the construct reliability (CR) and average variance extracted (AVE). The

construct reliability measures the reliability and internal consistency of measured variables representing

the latent construct while the average variance extracted measures the convergence among sets of items

representing a latent construct. The result showed that the CR values were greater than 0.7 while the AVE

values greater than 0.5. Hair et al. (2014) used the rule of thumb for construct validity; AVE must be 0.5

or greater to suggest adequate convergent validity and construct reliability (CR) should be 0.7 or higher to

specify adequate convergence or internal consistency. The construct validity of this research satisfied the

acceptance requirement.

Table 8. Construct reliability and average variance extracted

CR AVE MSV MaxR(H)

IF 0.826 0.520 0.759 0.698

FC 0.814 0.524 0.597 0.823

AD 0.865 0.617 0.612 0.879

IG 0.781 0.642 0.529 0.791

PG 0.895 0.591 0.271 0.911

The researchers evaluated the model fit on the basis of multiple indices – chi-square, chi-

square/df, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Normal Fit Index (NFI),

Comparative Fit Index, Tuker-Lewis Index (TLI) and Root mean squared error of approximation

(RMSEA). The result of the model fit shown in the table below.

Table 9 presents the summary of model fit indices. The results showed the chi-square/degrees of

freedom value of 1.866, a goodness of fit index value of 0.946, and adjusted goodness of fit index of

0.927, NFI value of 0.949; TLI value of 0.970 and CFI value of 0.976 and RMSEA value of 0.042. The

results supported the standard values recommended for chi-square/degree of freedom should exceed "1"

and should be less than "5" (Salisbury, Chin, Gopal, & Newsted, 2002). The GFI > 0.80 and AGFI > 0.80

(Hair, Anderson, Tatham, & Black, 2014), NFI > 0.90 or value > 0.80 (Schumacker and Lomax, 2010)

and TLI> 0.90 and RMSEA < 0.05 (Hair et al. 2014) were considered.

Table 9. Summary model fit indices of confirmatory factor analysis

Model Indices Values Standard

Chi-square/degrees of freedom 1.866 < 5

Goodness of Fit Index 0.946 > 0.80

Adjusted Goodness of Fit Index 0.927 > 0.80

Normal Fit Index (NFI) 0.949 > 0.80

Tuker- Lewis Index (TLI) 0.970 > 0.90

Comparative Fit Index (CFI) 0.976 > 0.90

Root mean square error of

approximation 0.042 < 0.05



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Structural Equation Model

R2 = 31.1%

Figure 2. Standardized Regression Weights for Factor Influencing the Retention Decision

Figure 2 illustrates the Standardized Regression Weights for the factors influencing the retention

decision. The researchers evaluated the model fit on the basis of multiple indices – chi-square, chi-

square/df, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Normal Fit Index (NFI),

Comparative Fit Index, Tuker-Lewis Index (TLI) and Root mean squared error of approximation

(RMSEA). The result of the model fit shows in the table below.

Table 9 presents the summary of model fit indices. The results showed the chi-square/degrees of

freedom value of 2.012 a goodness of fit index value of 0.921, and adjusted goodness of fit index of

0.899, NFI value of 0.911; TLI value of 0.944 and CFI value of 0.953 and RMSEA value of 0.045. The

results supported the recommended values for chi-square/degree of freedom which should exceed "1" and

should be less than "5" (Salisbury, Chin, Gopal, & Newsted, 2002). The GFI > 0.80 and AGFI > 0.80

(Hair, Anderson, Tatham, & Black, 2014), NFI > 0.90 or value > 0.80 (Schumacker and Lomax, 2010)

and TLI> 0.90 and RMSEA < 0.05 (Hair et al. 2014) are the acceptable values based on the standards.

The values identified in this research for different model indices satisfied the acceptable levels.

Table 10. Summary model fit indices of confirmatory factor analysis

Model Indices Values Standard

Chi-square/degrees of freedom 2.012 < 5

Goodness of Fit Index 0.921 > 0.80

Adjusted Goodness of Fit Index 0.899 > 0.80

Normal Fit Index (NFI) 0.911 > 0.80

Tuker- Lewis Index (TLI) 0.944 > 0.90

Comparative Fit Index (CFI) 0.953 > 0.90

Root mean square error of

approximation 0.045 < 0.05



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Hypothesis 1: Factors of Student’s Integration Theory affects the Retention Decision

This hypothesis was aimed to test the influenced of student’s integration theory in the retention

decision.

Table 11 presents the relationship between the student’s integration theory and retention decision.

Results showed that the faculty concern for student development and teaching positively influenced the

retention decision and statistically significant (b = 0.078, p < 0.05). The sets of interactions among

students, faculty, and staff that take place largely outside of the formal academic setting (Tinto, 1993).

Academic and intellectual development positively influence the retention decision and not statistically

significant (b=0.025, p > 0.05). Peer group interaction positively influence the retention decision and

statistically significant (b= 0.152, p value < 0.05). The results of Pascarella & Terenzini (2005) confirm

that the effect of peers on persistence has become more consistent in suggesting that positive experiences

between peers have a positive impact on the decision to stay in school. Interaction with faculty negatively

influence retention decision and statistically significant (b= -0.019, p value < 0.05). According to Tinto’s

model of student attrition, student-faculty interaction should increase not only the students’ social

integration but also their academic integration and this will lead to higher rate of retention (Pascarella &

Terenzini 2005). Institutional and goal commitment negatively influence the retention decision and

statistically significant (b = -0.853, p value < 0.001). The stronger the goal and institutional commitment

the more likely the student will graduate. Research shows that congruence between student goals and

institutional mission is mediated by academic and social components, and that increased integration into

academic and social campus communities causes greater institutional commitment and student persistence

(Tintos, 1993).

Table 11. Relationship of students integration theory in the retention decision

Variables Unstandardized Standardized

Exogenous Endogenous Estimate S.E C.R P Estimate

H1a. Faculty Concern for

Student Development

and Teaching

Retention

Decision

-0.174 0.087 -1.989 0.047 0.078

H1b. Academic and

Intellectual Development -0.037 0.051 -0.728 0.467 0.025

H1c. Peer Group

Interaction 0.084 0.026 3.221 0.001 0.152

H1d. Interaction with

Faculty 0.395 0.128 3.076 0.002 -0.019

H1e. Institutional and

Goal Commitment -0.462 0.040 -11.615 *** -0.853

***p value < 0.001; p-value <0.05 - Significant

Hypothesis 2: Demographic Profile affects the Retention Decision

Table 12 presents the relationship between the demographic profile and retention decision.

Results showed that the Age positively influenced the retention decision and statistically significant (b =

0.034, p > 0.05).The findings supported by Purdie (2007) that age could be used as predictor of student

retention. The age of college students may impact retention in a variety of ways including maturity of

student, work and life requirements, teaching methods, and whether or not the student has time to be

engaged in the campus Sex negatively influence the retention decision and statistically significant (b= -

0.074, p < 0.05). Grebennikov and Skaines (2009) found that women find academic goals more important

than men and they place a greater value on higher education, mainly because women need to better

prepare themselves in order to have the same chances on the job market. Year Level negatively influence



43


the retention decision and not statistically significant (b= -0.041 p value > 0.05). GWA/GPA negatively

influence retention decision and statistically significant (b= -0.065, p value < 0.05). This results

confirmed that grades is important predictor of student retention especially from first to second year

(Monday, 2015). Monthly income negatively influence the retention decision and not statistically

significant (b = -0.017, p value > 0.05). Higher annual and lifetime earnings are most frequently

compensated for attending college and continuing to complete a degree. But admission and persistence

decisions are guided by the availability of financial assistance for many low-income and minority students

(Educationalpolicy.org, 2004).

Table 12. Relationship of demographic profile and retention decision

Variables Unstandardized Standardized

Exogenous Endogenous Estimate S.E C.R P Estimate

H2a Age

Retention

Decision

0.022 0.023 0.947 0.343 0.034

H2b. Sex -0.054 0.023 -2.417 0.018 -0.074

H2c. Year Level -0.019 0.015 -1.248 0.261 -0.041

H2d. GWA/GPA -0.022 0.011 -1.917 0.041 -0.065

H2e. Monthly Income -0.003 0.007 -0.423 0.566 -0.017

***p value < 0.001; p-value <0.05 - Significant

CONCLUSIONS

Based on the findings of the study, the following conclusion were drawn: Majority of the

respondents were aged 20-22, female, 3rd year level, more than 50,000 monthly income and 2.01-2.50

general weighted average.

In the assessment of the factors influencing the retention decision of the respondents, institutional

and goal commitment had highest assessment while academic and intellectual development had the

lowest assessment. This means that means that students with high level of commitments were more likely

to stay. Using the exploratory and confirmatory factor analysis, the results were found to be acceptable

based on the standard requirements of the model indices. In terms of construct validity, all latent variables

satisfy the acceptance requirement of the model.

Faculty concern for student development and teaching, peer group interaction, interaction with

faculty and institutional and goal commitment were found to be the significant factors affecting the

retention decision of the students. The results supported by (Tinto’s, 1993) using student integration

theory suggest that academic integration, social integration, institutional commitment and goal

commitment have the strongest impact on student retention. While, sex and GWA/GPA in the

demographic profile of respondents were found to be significant factors that influence the retention

decision of the students.

Recommendations

Based on the conclusion of the study, the researchers suggested to strengthen the faculty and

student academic consultation and student tutorial services. The consultation will serve as venue between

the students and faculty to discuss matters related to academic difficulties of the students and also to

motivate the students to finish their degree on time while the student tutorial will help in motivating

students to complete their schooling and stay until they graduate. The researchers also suggested to the

Guidance Office in coordination with the faculty members to conduct regularly counseling to the students

especially in this time of pandemic. Regular checking of student feedback is also suggested to further

enhance the services offered by institution and to make the students fully satisfied during their entire stay

in the institution



44


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